Existence of Orthogonal Transformation for Given Sub-spaces in R^n

In summary, the problem deals with proving the existence of an orthogonal transformation between two sub-spaces of R^n, W_1 and W_2, where the dimensions of both sub-spaces are equal and non-zero. The proof involves constructing orthonormal bases for R^n from the bases of W_1 and W_2 using the Gram-Schmidt process. By carefully choosing the transformation T, it can be shown that T(W_1) = T(W_2), proving the existence of the orthogonal transformation.
  • #1
daniel_i_l
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Homework Statement


Given two sub-spaces of R^n - W_1 and W_2 where dimW_1 = dimW_2 =/= 0.
Prove that there exists an orthogonal transformation T:R^n -> R^n so that
T(W_1) = T(W_2)


Homework Equations





The Attempt at a Solution


If dimW_1 = dimW_2 = m then we can say that {v_1,...,v_m} is a basis of W_1 and {u_1,...,u_m} is a basis of W_2. We can make from these bases of R^n:
{v_1,...,v_m,a_1,...,a_{n-m}} is a basis of R^n and
{u_1,...,u_m,b_1,...,b_{n-m}} is also.
From these we can make orthonormal bases of R^n (via GS) so that:
{v'_1,...,v'_m,a'_1,...,a'_{n-m}} is an orthonormal basis of R^n and
{u'_1,...,u'_m,b'_1,...,b'_{n-m}} is also.
Now we make a transformation where:
T(v'_i) = u'_i
T(a'_i) = b'_i
Now, since the transformation of an orthonormal base gives another orthonormal base it's a ON transformation. And since {v'_1,...,v'_m} is a basis of W_1 and{u'_1,...,u'_m} is a basis of W_2 then T(W_1) = W_2
Is that a complete proof?
Thanks.
 
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  • #2
That's pretty much it. Though you may want to point out that you've diligently applied GS in such a way as to ensure that (v'_1..v'_m) and (u'_1...u'_m) are still a basis for W_1 and W_2. I.e. what order to do GS in? You are probably already thinking of the correct order. I'm just saying this because I can't think of anything else to fault and am wondering why you bothered to post this in the first place. It is really easy.
 
Last edited:
  • #3
Thanks for the comments :)
 

Related to Existence of Orthogonal Transformation for Given Sub-spaces in R^n

1. What is an orthogonal transformation?

An orthogonal transformation is a type of linear transformation in mathematics that preserves the length of vectors and the angles between them. It is also known as an orthogonal matrix or an isometry.

2. How is an orthogonal transformation different from other types of transformations?

Unlike other transformations, an orthogonal transformation does not change the length or direction of vectors. This means that it preserves the geometric properties of the original shape or object.

3. How is an orthogonal transformation represented?

An orthogonal transformation is typically represented by an orthogonal matrix, which is a square matrix with orthogonal columns (meaning they are perpendicular to each other) and a determinant of either 1 or -1.

4. What are some common applications of orthogonal transformations?

Orthogonal transformations are commonly used in fields such as computer graphics, physics, and engineering. They are essential for calculating rotations, reflections, and other geometric transformations accurately.

5. Are all orthogonal transformations reversible?

Yes, because orthogonal transformations preserve the length and angles of vectors, they are always reversible. This means that the original shape or object can be restored by applying the inverse of the transformation.

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